research-article

A Survey on Dialogue Systems: Recent Advances and New Frontiers

Authors:

Jiliang TangAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 19, Issue 2

Pages 25 - 35

https://doi.org/10.1145/3166054.3166058

Published: 21 November 2017 Publication History

Abstract

Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as computer vision, natural language processing, and recommender systems. For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum amount of hand-crafting. In this article, we give an overview to these recent advances on dialogue systems from various perspectives and discuss some possible research directions. In particular, we generally divide existing dialogue systems into task-oriented and nontask- oriented models, then detail how deep learning techniques help them with representative algorithms and finally discuss some appealing research directions that can bring the dialogue system research into a new frontier

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cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 19, Issue 2

December 2017

46 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/3166054

Editors:
Charu Aggarwal
IBM T.J. Watson
,
Haixun Wang
Google
,
Ankur Teredesai
University of Washington Tacoma
,
Hanghang Tong
Arizona State University

Issue’s Table of Contents

Copyright © 2017 Authors.

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Association for Computing Machinery

New York, NY, United States

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Published: 21 November 2017

Published in SIGKDD Volume 19, Issue 2

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Wang HGuo BZeng YChen MDing YZhang YYao LYu Z(2025)Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A ReviewACM Transactions on Information Systems10.1145/3715098Online publication date: 28-Jan-2025
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https://doi.org/10.1109/TASLP.2024.3507571
Guo JWang MYin HSong BChi YYu FYuen C(2025)Large Language Models and Artificial Intelligence Generated Content Technologies Meet Communication NetworksIEEE Internet of Things Journal10.1109/JIOT.2024.349649112:2(1529-1553)Online publication date: 15-Jan-2025
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Liu ZZhang WYang P(2025)Can AI chatbots effectively improve EFL learners’ learning effects?—A meta-analysis of empirical research from 2022–2024Computer Assisted Language Learning10.1080/09588221.2025.2456512(1-27)Online publication date: 8-Feb-2025
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Ohashi AHigashinaka R(2025)Optimizing pipeline task-oriented dialogue systems using post-processing networksComputer Speech & Language10.1016/j.csl.2024.10174290(101742)Online publication date: Mar-2025
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Algherairy AAhmed M(2025)Prompting large language models for user simulation in task-oriented dialogue systemsComputer Speech & Language10.1016/j.csl.2024.10169789(101697)Online publication date: Jan-2025
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